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Efficient Reinforcement Learning via Decoupling Exploration and Utilization

Jingpu Yang, Helin Wang, Qirui Zhao, Zhecheng Shi, Zirui Song, Miao Fang

TL;DR

This paper addresses the exploration–exploitation balance and generalization gaps in reinforcement learning by decoupling exploration and utilization through OPARL, which employs an optimistic Actor for exploration and a pessimistic Actor for exploitation. The method uses an ensemble of $Q$-values to drive optimistic exploration and conservative updates for stability, with periodic parameter resets between the two strategies. Empirically, OPARL demonstrates superior performance across Mujoco and DMControl benchmarks, outperforming strong baselines such as TD3, SAC, PPO, and REDQ on most tasks, and showing notable gains in particularly challenging settings. The work highlights the practical value of decoupled exploration and exploitation for improving sample efficiency, exploration coverage, and generalization in continuous-control RL problems.

Abstract

Reinforcement Learning (RL), recognized as an efficient learning approach, has achieved remarkable success across multiple fields and applications, including gaming, robotics, and autonomous vehicles. Classical single-agent reinforcement learning grapples with the imbalance of exploration and exploitation as well as limited generalization abilities. This methodology frequently leads to algorithms settling for suboptimal solutions that are tailored only to specific datasets. In this work, our aim is to train agent with efficient learning by decoupling exploration and utilization, so that agent can escaping the conundrum of suboptimal Solutions. In reinforcement learning, the previously imposed pessimistic punitive measures have deprived the model of its exploratory potential, resulting in diminished exploration capabilities. To address this, we have introduced an additional optimistic Actor to enhance the model's exploration ability, while employing a more constrained pessimistic Actor for performance evaluation. The above idea is implemented in the proposed OPARL (Optimistic and Pessimistic Actor Reinforcement Learning) algorithm. This unique amalgamation within the reinforcement learning paradigm fosters a more balanced and efficient approach. It facilitates the optimization of policies that concentrate on high-reward actions via pessimistic exploitation strategies while concurrently ensuring extensive state coverage through optimistic exploration. Empirical and theoretical investigations demonstrate that OPARL enhances agent capabilities in both utilization and exploration. In the most tasks of DMControl benchmark and Mujoco environment, OPARL performed better than state-of-the-art methods. Our code has released on https://github.com/yydsok/OPARL

Efficient Reinforcement Learning via Decoupling Exploration and Utilization

TL;DR

This paper addresses the exploration–exploitation balance and generalization gaps in reinforcement learning by decoupling exploration and utilization through OPARL, which employs an optimistic Actor for exploration and a pessimistic Actor for exploitation. The method uses an ensemble of -values to drive optimistic exploration and conservative updates for stability, with periodic parameter resets between the two strategies. Empirically, OPARL demonstrates superior performance across Mujoco and DMControl benchmarks, outperforming strong baselines such as TD3, SAC, PPO, and REDQ on most tasks, and showing notable gains in particularly challenging settings. The work highlights the practical value of decoupled exploration and exploitation for improving sample efficiency, exploration coverage, and generalization in continuous-control RL problems.

Abstract

Reinforcement Learning (RL), recognized as an efficient learning approach, has achieved remarkable success across multiple fields and applications, including gaming, robotics, and autonomous vehicles. Classical single-agent reinforcement learning grapples with the imbalance of exploration and exploitation as well as limited generalization abilities. This methodology frequently leads to algorithms settling for suboptimal solutions that are tailored only to specific datasets. In this work, our aim is to train agent with efficient learning by decoupling exploration and utilization, so that agent can escaping the conundrum of suboptimal Solutions. In reinforcement learning, the previously imposed pessimistic punitive measures have deprived the model of its exploratory potential, resulting in diminished exploration capabilities. To address this, we have introduced an additional optimistic Actor to enhance the model's exploration ability, while employing a more constrained pessimistic Actor for performance evaluation. The above idea is implemented in the proposed OPARL (Optimistic and Pessimistic Actor Reinforcement Learning) algorithm. This unique amalgamation within the reinforcement learning paradigm fosters a more balanced and efficient approach. It facilitates the optimization of policies that concentrate on high-reward actions via pessimistic exploitation strategies while concurrently ensuring extensive state coverage through optimistic exploration. Empirical and theoretical investigations demonstrate that OPARL enhances agent capabilities in both utilization and exploration. In the most tasks of DMControl benchmark and Mujoco environment, OPARL performed better than state-of-the-art methods. Our code has released on https://github.com/yydsok/OPARL
Paper Structure (16 sections, 6 equations, 3 figures, 1 table, 1 algorithm)

This paper contains 16 sections, 6 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. Reinforcement Learning
  4. Sample Efficient Reinforcement Learning
  5. Optimistic and Pessimistic in Reinforcement Learning
  6. PROBLEM SETUP
  7. Optimistic and Pessimistic Actors in RL
  8. The OPARL Framework
  9. The Algorithm of OPARL
  10. Parameter Reset
  11. EXPERIMENTS
  12. Evaluation Setting
  13. Evaluation of OPARL in Mujoco Environment
  14. Evaluation of OPARL in DMControl Environment
  15. Ablation Study
  16. ...and 1 more sections

Figures (3)

  • Figure 1: The performance curves presented in Mujoco environment, including our OPARL and other benchmark algorithms within Mujoco framework. The shaded region represents half a standard deviation of the average evaluation over 5 distinct seeds. Curves are smoothed with a window of size 10 for visual clarity.
  • Figure 2: The ablation performance curves presented in the Mujoco environment. The shaded region represents half a standard deviation of the average evaluation over 5 distinct seeds. Curves are smoothed with a window of size 10 for visual clarity.
  • Figure :